DOCSLIB.ORG

Robot Vision: Projective Geometry

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Robot Vision: Projective Geometry Robot Vision: Projective Geometry Ass.Prof. Friedrich Fraundorfer SS 2018 1 Learning goals . Understand homogeneous coordinates . Understand points, line, plane parameters and interpret them geometrically . Understand point, line, plane interactions geometrically . Analytical calculations with lines, points and planes . Understand the difference between Euclidean and projective space . Understand the properties of parallel lines and planes in projective space . Understand the concept of the line and plane at infinity 2 Outline . 1D projective geometry . 2D projective geometry ▫ Homogeneous coordinates ▫ Points, Lines ▫ Duality . 3D projective geometry ▫ Points, Lines, Planes ▫ Duality ▫ Plane at infinity 3 Literature . Multiple View Geometry in Computer Vision. Richard Hartley and Andrew Zisserman. Cambridge University Press, March 2004. Mundy, J.L. and Zisserman, A., Geometric Invariance in Computer Vision, Appendix: Projective Geometry for Machine Vision, MIT Press, Cambridge, MA, 1992 . Available online: www.cs.cmu.edu/~ph/869/papers/zisser-mundy.pdf 4 Motivation – Image formation [Source: Charles Gunn] 5 Motivation – Parallel lines [Source: Flickr] 6 Motivation – Epipolar constraint X world point epipolar plane x x’ x‘TEx=0 C T C’ R 7 Euclidean geometry vs. projective geometry Definitions: . Geometry is the teaching of points, lines, planes and their relationships and properties (angles) . Geometries are defined based on invariances (what is changing if you transform a configuration of points, lines etc.) . Geometric transformations of Euclidean geometry preserve distances . Geometric transformations of projective geometry do NOT preserve distances . Projective geometry was developed to explain the perspective changes of three-dimensional objects when projected to a plane. 8 Difference between Euclidean and projective transformation Euclidean transformation Projective transformation 9 1D Euclidean geometry x p1 p2 p3 p4 Euclidean coordinate: p1=[x] 10 1D projective geometry w W=1 p1 p2 p3 p4 x homogeneous coordinate: p1=[x,w] ≈ [x,1] 11 2D projective geometry . Homogeneous coordinates . Points, Lines . Duality 12 Homogeneous coordinates . projective plane = Euclidean plane + a new line of points . The projective space associated to R3 is called the projective plane P2. w y ℝ 2 O2 p x 1 v O3 u image coordinate: p=[x,y] homogeneous coordinate: p=[u,v,w] ≈ [u,v,1] 13 Points . A point in the image is a ray in projective space -y (wx,wy,w) (x,y,1) (0,0,0) x -z image plane • Each point (x,y) on the plane is represented by a ray (wx,wy,w) – all points on the ray are equivalent: (x, y, 1) (wx, wy, w) 14 Lines . A line in the image plane is defined by the equation ax + by + cz = 0 in projective space . [a,b,c] are the line parameters . A point [x,y,1] lies on the line if the equation ax + by + cz = 0 is satisfied . This can be written in vector notation with a dot product: x 0 a b cy z lT p . A line is also represented as a homogeneous 3-vector l 15 Calculations with lines and points . Defining a line by two points 푙 = 푥 × 푦 . Intersection of two lines 푥 = 푙 × 푚 . Proof: 푙 = 푥 × 푦 푥푇 푥 × 푦 = 푦푇 푥 × 푦 = 0 (scalar triple product) 푥푇푙 = 푦푇푙 = 0 16 Geometric interpretation of line parameters [a,b,c] ▫ A line l is a homogeneous 3-vector, which is a ray in projective space ▫ It is to every point (ray) p on the line: l p=0 p p 2 l 1 What is the line l spanned by rays p1 and p2 ? • l is to p1 and p2 l = p1 p2 • l is the plane normal 17 Point and line duality Duality principle: . To any theorem of 2-dimensional projective geometry there corresponds a dual theorem, which may be derived by interchanging the role of points and lines in the original theorem x l xTl 0 lTx 0 x l l' l x x' 18 Point and line duality p p2 l1 p l 1 l2 What is the line l spanned by rays p1 and p2 ? • l is to p1 and p2 l = p1 p2 • l is the plane normal What is the intersection of two lines l1 and l2 ? • p is to l1 and l2 p = l1 l2 Points and lines are dual in projective space • given any formula, can switch the meanings of points and 19 lines to get another formula Intersection of parallel lines . l and m are two parallel lines 푙 = (푎, 푏, 푐)푇 푒. 푔. (−1,0,1)푇 (a line parallel to y-axis) 푚 = (푎, 푏, 푑)푇 푒. 푔. (−1,0,2)푇 (another line parallel to y-axis) . Intersection of l and m 푥 = 푙 × 푚 푎 푎 푏푑 − 푏푐 푏 x 1 x 2 푥 = 푏 × 푏 = 푎푐 − 푎푑 = 푑 − 푐 −푎 푐 푑 푎푏 − 푎푏 0 . A point (x,y,0) is called an ideal point, it does not lie in the image plane. But where does it lie then 20 Ideal points and line at infinity -v (wx,wy,0) -w u image plane . Ideal point (“point at infinity”) ▫ p (x, y, 0) – parallel to image plane ▫ It has infinite image coordinates . All ideal points lie at the line at infinity . l (0, 0, 1) – normal to the image plane . Why is it called a line at infinity? 21 Projective transformations . Mapping between planes x’=Hx x x’ O y’ 휋 y x’ x 22 Projective transformations Definition: Projective transformation x' h h h x 1 11 12 13 1 x' h h h x 2 21 22 23 2 or x' H x x'3 h31 h32 h33 x3 8DOF projectivity=collineation=projective transformation=homography To transform a point: p’ = Hp To transform a line: lp=0 l’p’=0 0 = lp = lH-1Hp = lH-1p’ l’ = lH-1 lines are transformed by postmultiplication of H-1 23 Overview 2D transformations h11 h12 h13 Concurrency, collinearity, Projective h h h order of contact (intersection, 8dof 21 22 23 tangency, inflection, etc.), cross ratio h31 h32 h33 Parallellism, ratio of areas, a11 a12 tx ratio of lengths on parallel Affine a a t lines (e.g midpoints), linear 6dof 21 22 y combinations of vectors 0 0 1 (centroids). sr sr t 11 12 x Ratios of lengths, angles. Similarity sr sr t 4dof 21 22 y 0 0 1 r11 r12 tx Euclidean r r t lengths, areas. 3dof 21 22 y 0 0 1 24 Effects of projective transformations . Foreshortening effects can be imaged easily with primitive shapes . But, how does an circle get transformed? 25 Effects of projective transformations Center of projected circle Ellipse center 2D circle Circle after projective transformation 26 3D projective geometry . Points, Lines, Planes . Duality . Plane at infinity . Image formation 27 3D projective geometry . The concepts of 2D generalize naturally to 3D ▫ The axioms of geometry can be applied to 3D as well . 3D projective space = 3D Euclidean space + plane at infinity ▫ Not so simple to visualize anymore (4D space) . Entities are now points, lines and planes ▫ Projective 3D points have four coordinates: P = (x,y,z,w) . Points, lines, and planes lead to more intersection and joining options that in the 2D case 28 Planes . Plane equation Π1푋+Π2푌 + Π3푍+Π4=0 Π푇 푋 =0 ▫ Expresses that point X is on plane Π . Plane parameters Π = [Π1, Π2, Π3, Π4] ▫ Plane parameters are normal vector + distance from origin 29 Join and incidence relations with planes . A plane is defined uniquely by the join of three points, or the join of a line and point in general position . Two distinct planes intersect in a unique line . Three distinct planes intersect in a unique point 30 Three points define a plane . X1,X2,X3 are three distinct points, each has to fullfil the incidence equation. Equations can be stacked. 푇 푋1 푇 푋2 Π = 0 (3푥4)(4푥1) 푇 푋3 ▫ Plane parameters are the solution vector to this linear equation system (e.g. SVD) . Points and planes are dual 푇 Π1 푇 Π2 푋 = 0 푇 Π3 31 Lines . Lines are complicated . Lines and points are not dual in 3D projective space . Lines are represented by a 4x4 matrix, called Plücker matrix . Computation of the line matrix from two points A,B 퐿 = 퐴퐵푇 − 퐵퐴푇 4푥4 푚푎푡푟푖푥 . Matrix is skew-symmetric . Example line of the x-axis ▫ x1 = [0 0 0 1]T x2 = [1 0 0 1]T L = x1*x2T-x2*x1T L = 0 0 0 -1 0 0 0 0 0 0 0 0 1 0 0 0 32 Lines . Points and planes are dual, we can get new equations by substituting points with planes 퐿 = 퐴퐵푇 − 퐵퐴푇 퐴, 퐵 푎푟푒 푝표푖푛푡푠 퐿∗ = 푃푄푇 − 푄푃푇 푃, 푄 푎푟푒 푝푙푎푛푒푠 . The intersection of two planes P,Q is a line . Lines are self dual, the same line L has a dual representation L* . The matrix L can be directly computed from the entries of L* 33 Point, planes and lines . A plane can be defined by the join of a point X and a line L Π = 퐿∗푋 . A point can be defined by the intersection of a plane with a line L X = LΠ 34 Plane at infinity . Parallel lines and parallel planes intersect at Π∞ . Plane parameters of Π∞ 푇 Π∞ = (0,0,0,1) . It is a plane that contains all the direction vectors 퐷 = (푥1, 푥2, 푥3,0)푇, vectors that originate from the origin of 4D space . Try to imagine an extension of the 2D case (see illustration below) to the 3D case… -v (wx,wy,0) -w u image plane 35 Recap - Learning goals . Understand homogeneous coordinates . Understand points, line, plane parameters and interpret them geometrically .
Load more

Recommended publications
  • Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration
    Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration Manmohan Chandrakery Sameer Agarwalz David Kriegmany Serge Belongiey [email protected] [email protected] [email protected] [email protected] y University of California, San Diego z University of Washington, Seattle Abstract parameters of the cameras, which is commonly approached by estimating the dual image of the absolute conic (DIAC). We present a practical, stratified autocalibration algo- A variety of linear methods exist towards this end, how- rithm with theoretical guarantees of global optimality. Given ever, they are known to perform poorly in the presence of a projective reconstruction, the first stage of the algorithm noise [10]. Perhaps more significantly, most methods a pos- upgrades it to affine by estimating the position of the plane teriori impose the positive semidefiniteness of the DIAC, at infinity. The plane at infinity is computed by globally which might lead to a spurious calibration. Thus, it is im- minimizing a least squares formulation of the modulus con- portant to impose the positive semidefiniteness of the DIAC straints. In the second stage, the algorithm upgrades this within the optimization, not as a post-processing step. affine reconstruction to a metric one by globally minimizing This paper proposes global minimization algorithms for the infinite homography relation to compute the dual image both stages of stratified autocalibration that furnish theoreti- of the absolute conic (DIAC). The positive semidefiniteness cal certificates of optimality. That is, they return a solution at of the DIAC is explicitly enforced as part of the optimization most away from the global minimum, for arbitrarily small .
    [Show full text]
  • Projective Geometry: a Short Introduction
    Projective Geometry: A Short Introduction Lecture Notes Edmond Boyer Master MOSIG Introduction to Projective Geometry Contents 1 Introduction 2 1.1 Objective . .2 1.2 Historical Background . .3 1.3 Bibliography . .4 2 Projective Spaces 5 2.1 Definitions . .5 2.2 Properties . .8 2.3 The hyperplane at infinity . 12 3 The projective line 13 3.1 Introduction . 13 3.2 Projective transformation of P1 ................... 14 3.3 The cross-ratio . 14 4 The projective plane 17 4.1 Points and lines . 17 4.2 Line at infinity . 18 4.3 Homographies . 19 4.4 Conics . 20 4.5 Affine transformations . 22 4.6 Euclidean transformations . 22 4.7 Particular transformations . 24 4.8 Transformation hierarchy . 25 Grenoble Universities 1 Master MOSIG Introduction to Projective Geometry Chapter 1 Introduction 1.1 Objective The objective of this course is to give basic notions and intuitions on projective geometry. The interest of projective geometry arises in several visual comput- ing domains, in particular computer vision modelling and computer graphics. It provides a mathematical formalism to describe the geometry of cameras and the associated transformations, hence enabling the design of computational ap- proaches that manipulates 2D projections of 3D objects. In that respect, a fundamental aspect is the fact that objects at infinity can be represented and manipulated with projective geometry and this in contrast to the Euclidean geometry. This allows perspective deformations to be represented as projective transformations. Figure 1.1: Example of perspective deformation or 2D projective transforma- tion. Another argument is that Euclidean geometry is sometimes difficult to use in algorithms, with particular cases arising from non-generic situations (e.g.
    [Show full text]
  • COMBINATORICS, Volume
    http://dx.doi.org/10.1090/pspum/019 PROCEEDINGS OF SYMPOSIA IN PURE MATHEMATICS Volume XIX COMBINATORICS AMERICAN MATHEMATICAL SOCIETY Providence, Rhode Island 1971 Proceedings of the Symposium in Pure Mathematics of the American Mathematical Society Held at the University of California Los Angeles, California March 21-22, 1968 Prepared by the American Mathematical Society under National Science Foundation Grant GP-8436 Edited by Theodore S. Motzkin AMS 1970 Subject Classifications Primary 05Axx, 05Bxx, 05Cxx, 10-XX, 15-XX, 50-XX Secondary 04A20, 05A05, 05A17, 05A20, 05B05, 05B15, 05B20, 05B25, 05B30, 05C15, 05C99, 06A05, 10A45, 10C05, 14-XX, 20Bxx, 20Fxx, 50A20, 55C05, 55J05, 94A20 International Standard Book Number 0-8218-1419-2 Library of Congress Catalog Number 74-153879 Copyright © 1971 by the American Mathematical Society Printed in the United States of America All rights reserved except those granted to the United States Government May not be produced in any form without permission of the publishers Leo Moser (1921-1970) was active and productive in various aspects of combin• atorics and of its applications to number theory. He was in close contact with those with whom he had common interests: we will remember his sparkling wit, the universality of his anecdotes, and his stimulating presence. This volume, much of whose content he had enjoyed and appreciated, and which contains the re• construction of a contribution by him, is dedicated to his memory. CONTENTS Preface vii Modular Forms on Noncongruence Subgroups BY A. O. L. ATKIN AND H. P. F. SWINNERTON-DYER 1 Selfconjugate Tetrahedra with Respect to the Hermitian Variety xl+xl + *l + ;cg = 0 in PG(3, 22) and a Representation of PG(3, 3) BY R.
    [Show full text]
  • Collineations in Perspective
    Collineations in Perspective Now that we have a decent grasp of one-dimensional projectivities, we move on to their two di- mensional analogs. Although they are more complicated, in a sense, they may be easier to grasp because of the many applications to perspective drawing. Speaking of, let's return to the triangle on the window and its shadow in its full form instead of only looking at one line. Perspective Collineation In one dimension, a perspectivity is a bijective mapping from a line to a line through a point. In two dimensions, a perspective collineation is a bijective mapping from a plane to a plane through a point. To illustrate, consider the triangle on the window plane and its shadow on the ground plane as in Figure 1. We can see that every point on the triangle on the window maps to exactly one point on the shadow, but the collineation is from the entire window plane to the entire ground plane. We understand the window plane to extend infinitely in all directions (even going through the ground), the ground also extends infinitely in all directions (we will assume that the earth is flat here), and we map every point on the window to a point on the ground. Looking at Figure 2, we see that the lamp analogy breaks down when we consider all lines through O. Although it makes sense for the base of the triangle on the window mapped to its shadow on 1 the ground (A to A0 and B to B0), what do we make of the mapping C to C0, or D to D0? C is on the window plane, underground, while C0 is on the ground.
    [Show full text]
  • Feature Matching and Heat Flow in Centro-Affine Geometry
    Symmetry, Integrability and Geometry: Methods and Applications SIGMA 16 (2020), 093, 22 pages Feature Matching and Heat Flow in Centro-Affine Geometry Peter J. OLVER y, Changzheng QU z and Yun YANG x y School of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA E-mail: [email protected] URL: http://www.math.umn.edu/~olver/ z School of Mathematics and Statistics, Ningbo University, Ningbo 315211, P.R. China E-mail: [email protected] x Department of Mathematics, Northeastern University, Shenyang, 110819, P.R. China E-mail: [email protected] Received April 02, 2020, in final form September 14, 2020; Published online September 29, 2020 https://doi.org/10.3842/SIGMA.2020.093 Abstract. In this paper, we study the differential invariants and the invariant heat flow in centro-affine geometry, proving that the latter is equivalent to the inviscid Burgers' equa- tion. Furthermore, we apply the centro-affine invariants to develop an invariant algorithm to match features of objects appearing in images. We show that the resulting algorithm com- pares favorably with the widely applied scale-invariant feature transform (SIFT), speeded up robust features (SURF), and affine-SIFT (ASIFT) methods. Key words: centro-affine geometry; equivariant moving frames; heat flow; inviscid Burgers' equation; differential invariant; edge matching 2020 Mathematics Subject Classification: 53A15; 53A55 1 Introduction The main objective in this paper is to study differential invariants and invariant curve flows { in particular the heat flow { in centro-affine geometry. In addition, we will present some basic applications to feature matching in camera images of three-dimensional objects, comparing our method with other popular algorithms.
    [Show full text]
  • Performance Evaluation of Genetic Algorithm and Simulated Annealing in Solving Kirkman Schoolgirl Problem
    FUOYE Journal of Engineering and Technology (FUOYEJET), Vol. 5, Issue 2, September 2020 ISSN: 2579-0625 (Online), 2579-0617 (Paper) Performance Evaluation of Genetic Algorithm and Simulated Annealing in solving Kirkman Schoolgirl Problem *1Christopher A. Oyeleye, 2Victoria O. Dayo-Ajayi, 3Emmanuel Abiodun and 4Alabi O. Bello 1Department of Information Systems, Ladoke Akintola University of Technology, Ogbomoso, Nigeria 2Department of Computer Science, Ladoke Akintola University of Technology, Ogbomoso, Nigeria 3Department of Computer Science, Kwara State Polytechnic, Ilorin, Nigeria 4Department of Mathematical and Physical Sciences, Afe Babalola University, Ado-Ekiti, Nigeria [email protected]|[email protected]|[email protected]|[email protected] Received: 15-FEB-2020; Reviewed: 12-MAR-2020; Accepted: 28-MAY-2020 http://dx.doi.org/10.46792/fuoyejet.v5i2.477 Abstract- This paper provides performance evaluation of Genetic Algorithm and Simulated Annealing in view of their software complexity and simulation runtime. Kirkman Schoolgirl is about arranging fifteen schoolgirls into five triplets in a week with a distinct constraint of no two schoolgirl must walk together in a week. The developed model was simulated using MATLAB version R2015a. The performance evaluation of both Genetic Algorithm (GA) and Simulated Annealing (SA) was carried out in terms of program size, program volume, program effort and the intelligent content of the program. The results obtained show that the runtime for GA and SA are 11.23sec and 6.20sec respectively. The program size for GA and SA are 2.01kb and 2.21kb, respectively. The lines of code for GA and SA are 324 and 404, respectively. The program volume for GA and SA are 1121.58 and 3127.92, respectively.
    [Show full text]
  • Blocking Set Free Configurations and Their Relations to Digraphs and Hypergraphs
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector DISCRETE MATHEMATICS ELSIZI’IER Discrete Mathematics 1651166 (1997) 359.-370 Blocking set free configurations and their relations to digraphs and hypergraphs Harald Gropp* Mihlingstrasse 19. D-69121 Heidelberg, German> Abstract The current state of knowledge concerning the existence of blocking set free configurations is given together with a short history of this problem which has also been dealt with in terms of digraphs without even dicycles or 3-chromatic hypergraphs. The question is extended to the case of nonsymmetric configurations (u,, b3). It is proved that for each value of I > 3 there are only finitely many values of u for which the existence of a blocking set free configuration is still unknown. 1. Introduction In the language of configurations the existence of blocking sets was investigated in [3, 93 for the first time. Further papers [4, 201 yielded the result that the existence problem for blocking set free configurations v3 has been nearly solved. There are only 8 values of v for which it is unsettled whether there is a blocking set free configuration ~7~:u = 15,16,17,18,20,23,24,26. The existence of blocking set free configurations is equivalent to the existence of certain hypergraphs and digraphs. In these two languages results have been obtained much earlier. In Section 2 the relations between configurations, hypergraphs, and digraphs are exhibited. Furthermore, a nearly forgotten result of Steinitz is described In his dissertation of 1894 Steinitz proved the existence of a l-factor in a regular bipartite graph 20 years earlier then Kiinig.
    [Show full text]
  • Finite Projective Geometries 243
    FINITE PROJECTÎVEGEOMETRIES* BY OSWALD VEBLEN and W. H. BUSSEY By means of such a generalized conception of geometry as is inevitably suggested by the recent and wide-spread researches in the foundations of that science, there is given in § 1 a definition of a class of tactical configurations which includes many well known configurations as well as many new ones. In § 2 there is developed a method for the construction of these configurations which is proved to furnish all configurations that satisfy the definition. In §§ 4-8 the configurations are shown to have a geometrical theory identical in most of its general theorems with ordinary projective geometry and thus to afford a treatment of finite linear group theory analogous to the ordinary theory of collineations. In § 9 reference is made to other definitions of some of the configurations included in the class defined in § 1. § 1. Synthetic definition. By a finite projective geometry is meant a set of elements which, for sugges- tiveness, are called points, subject to the following five conditions : I. The set contains a finite number ( > 2 ) of points. It contains subsets called lines, each of which contains at least three points. II. If A and B are distinct points, there is one and only one line that contains A and B. HI. If A, B, C are non-collinear points and if a line I contains a point D of the line AB and a point E of the line BC, but does not contain A, B, or C, then the line I contains a point F of the line CA (Fig.
    [Show full text]
  • Finite Projective Geometry 2Nd Year Group Project
    Finite Projective Geometry 2nd year group project. B. Doyle, B. Voce, W.C Lim, C.H Lo Mathematics Department - Imperial College London Supervisor: Ambrus Pal´ June 7, 2015 Abstract The Fano plane has a strong claim on being the simplest symmetrical object with inbuilt mathematical structure in the universe. This is due to the fact that it is the smallest possible projective plane; a set of points with a subsets of lines satisfying just three axioms. We will begin by developing some theory direct from the axioms and uncovering some of the hidden (and not so hidden) symmetries of the Fano plane. Alternatively, some projective planes can be derived from vector space theory and we shall also explore this and the associated linear maps on these spaces. Finally, with the help of some theory of quadratic forms we will give a proof of the surprising Bruck-Ryser theorem, which shows that if a projective plane has order n congruent to 1 or 2 mod 4, then n is the sum of two squares. Thus we will have demonstrated fascinating links between pure mathematical disciplines by incorporating the use of linear algebra, group the- ory and number theory to explain the geometric world of projective planes. 1 Contents 1 Introduction 3 2 Basic Defintions and results 4 3 The Fano Plane 7 3.1 Isomorphism and Automorphism . 8 3.2 Ovals . 10 4 Projective Geometry with fields 12 4.1 Constructing Projective Planes from fields . 12 4.2 Order of Projective Planes over fields . 14 5 Bruck-Ryser 17 A Appendix - Rings and Fields 22 2 1 Introduction Projective planes are geometrical objects that consist of a set of elements called points and sub- sets of these elements called lines constructed following three basic axioms which give the re- sulting object a remarkable level of symmetry.
    [Show full text]
  • The Dual Theorem Concerning Aubert Line
    The Dual Theorem concerning Aubert Line Professor Ion Patrascu, National College "Buzeşti Brothers" Craiova - Romania Professor Florentin Smarandache, University of New Mexico, Gallup, USA In this article we introduce the concept of Bobillier transversal of a triangle with respect to a point in its plan; we prove the Aubert Theorem about the collinearity of the orthocenters in the triangles determined by the sides and the diagonals of a complete quadrilateral, and we obtain the Dual Theorem of this Theorem. Theorem 1 (E. Bobillier) Let 퐴퐵퐶 be a triangle and 푀 a point in the plane of the triangle so that the perpendiculars taken in 푀, and 푀퐴, 푀퐵, 푀퐶 respectively, intersect the sides 퐵퐶, 퐶퐴 and 퐴퐵 at 퐴푚, 퐵푚 and 퐶푚. Then the points 퐴푚, 퐵푚 and 퐶푚 are collinear. 퐴푚퐵 Proof We note that = 퐴푚퐶 aria (퐵푀퐴푚) (see Fig. 1). aria (퐶푀퐴푚) 1 Area (퐵푀퐴푚) = ∙ 퐵푀 ∙ 푀퐴푚 ∙ 2 sin(퐵푀퐴푚̂ ). 1 Area (퐶푀퐴푚) = ∙ 퐶푀 ∙ 푀퐴푚 ∙ 2 sin(퐶푀퐴푚̂ ). Since 1 3휋 푚(퐶푀퐴푚̂ ) = − 푚(퐴푀퐶̂ ), 2 it explains that sin(퐶푀퐴푚̂ ) = − cos(퐴푀퐶̂ ); 휋 sin(퐵푀퐴푚̂ ) = sin (퐴푀퐵̂ − ) = − cos(퐴푀퐵̂ ). 2 Therefore: 퐴푚퐵 푀퐵 ∙ cos(퐴푀퐵̂ ) = (1). 퐴푚퐶 푀퐶 ∙ cos(퐴푀퐶̂ ) In the same way, we find that: 퐵푚퐶 푀퐶 cos(퐵푀퐶̂ ) = ∙ (2); 퐵푚퐴 푀퐴 cos(퐴푀퐵̂ ) 퐶푚퐴 푀퐴 cos(퐴푀퐶̂ ) = ∙ (3). 퐶푚퐵 푀퐵 cos(퐵푀퐶̂ ) The relations (1), (2), (3), and the reciprocal Theorem of Menelaus lead to the collinearity of points 퐴푚, 퐵푚, 퐶푚. Note Bobillier's Theorem can be obtained – by converting the duality with respect to a circle – from the theorem relative to the concurrency of the heights of a triangle.
    [Show full text]
  • Cramer Benjamin PMET
    Just-in-Time-Teaching and other gadgets Richard Cramer-Benjamin Niagara University http://faculty.niagara.edu/richcb The Class MAT 443 – Euclidean Geometry 26 Students 12 Secondary Ed (9-12 or 5-12 Certification) 14 Elementary Ed (1-6, B-6, or 1-9 Certification) The Class Venema, G., Foundations of Geometry , Preliminaries/Discrete Geometry 2 weeks Axioms of Plane Geometry 3 weeks Neutral Geometry 3 weeks Euclidean Geometry 3 weeks Circles 1 week Transformational Geometry 2 weeks Other Sources Requiring Student Questions on the Text Bonnie Gold How I (Finally) Got My Calculus I Students to Read the Text Tommy Ratliff MAA Inovative Teaching Exchange JiTT Just-in-Time-Teaching Warm-Ups Physlets Puzzles On-line Homework Interactive Lessons JiTTDLWiki JiTTDLWiki Goals Teach Students to read a textbook Math classes have taught students not to read the text. Get students thinking about the material Identify potential difficulties Spend less time lecturing Example Questions For February 1 Subject line WarmUp 3 LastName Due 8:00 pm, Tuesday, January 31. Read Sections 5.1-5.4 Be sure to understand The different axiomatic systems (Hilbert's, Birkhoff's, SMSG, and UCSMP), undefined terms, Existence Postulate, plane, Incidence Postulate, lie on, parallel, the ruler postulate, between, segment, ray, length, congruent, Theorem 5.4.6*, Corrollary 5.4.7*, Euclidean Metric, Taxicab Metric, Coordinate functions on Euclidean and taxicab metrics, the rational plane. Questions Compare Hilbert's axioms with the UCSMP axioms in the appendix. What are some observations you can make? What is a coordinate function? What does it have to do with the ruler placement postulate? What does the rational plane model demonstrate? List 3 statements about the reading.
    [Show full text]
  • The Generalization of Miquels Theorem
    THE GENERALIZATION OF MIQUEL’S THEOREM ANDERSON R. VARGAS Abstract. This papper aims to present and demonstrate Clifford’s version for a generalization of Miquel’s theorem with the use of Euclidean geometry arguments only. 1. Introduction At the end of his article, Clifford [1] gives some developments that generalize the three circles version of Miquel’s theorem and he does give a synthetic proof to this generalization using arguments of projective geometry. The series of propositions given by Clifford are in the following theorem: Theorem 1.1. (i) Given three straight lines, a circle may be drawn through their intersections. (ii) Given four straight lines, the four circles so determined meet in a point. (iii) Given five straight lines, the five points so found lie on a circle. (iv) Given six straight lines, the six circles so determined meet in a point. That can keep going on indefinitely, that is, if n ≥ 2, 2n straight lines determine 2n circles all meeting in a point, and for 2n +1 straight lines the 2n +1 points so found lie on the same circle. Remark 1.2. Note that in the set of given straight lines, there is neither a pair of parallel straight lines nor a subset with three straight lines that intersect in one point. That is being considered all along the work, without further ado. arXiv:1812.04175v1 [math.HO] 11 Dec 2018 In order to prove this generalization, we are going to use some theorems proposed by Miquel [3] and some basic lemmas about a bunch of circles and their intersections, and we will follow the idea proposed by Lebesgue[2] in a proof by induction.
    [Show full text]